Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. This article reviews the application of artificial intelligence (AI) to enhance pre-, during-, and post-AM processes to meet a wider range of subject-specific requirements of healthcare interventions. This article introduces common AM processes and AI tools, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of AI in pre-processing is described in the core dimensions like structural design and image reconstruction, material design and formulations, and processing parameters. The role of AI in a printing process is described based on hardware specifications, printing configurations, and core operational parameters such as temperature. Likewise, the post-processing describes the role of AI for surface finishing, dimensional accuracy, curing processes, and a relationship between AM processes and bioactivity. The later sections provide detailed scientometric studies, thematic evaluation of the subject topic, and also reflect on AI ethics in AM for biomedical applications. This review article perceives AI as a robust and powerful tool for AM of biomedical products. From tissue engineering (TE) to prosthesis, lab-on-chip to organs-on-a-chip, and additive biofabrication for range of products; AI holds a high potential to screen desired process-property-performance relationships for resource-efficient pre- to post-AM cycle to develop high-quality healthcare products with enhanced subject-specific compliance specification.

From conception to consumption: Applications of semi-solid extrusion 3D printing in oral drug delivery

Semi-Solid Extrusion 3D printing (SSE 3DP) has emerged as a promising technology for fabricating oral drug formulations, offering significant opportunities for personalized medicine and tailored therapeutic outcomes. SSE 3DP is particularly advantageous for producing soft and chewable drug products and is well-suited for formulations containing thermosensitive drugs due to its low-temperature printing process. Among various 3D printing techniques, SSE 3DP holds considerable potential for point-of-care applications, enabling the on-demand production of patient-specific dosage forms. Despite these advantages, SSE 3DP faces certain limitations that affect its overall development and widespread adoption. This review provides a comprehensive overview of SSE 3DP's fundamental principles, current applications, and future prospects in oral drug delivery. It also addresses the challenges and limitations associated with SSE 3DP and examines the current outlook of this technique in oral drug delivery applications. An example of such a challenge is the lack of a harmonized method for evaluating rheological properties. To address this issue, the review describes a methodology for obtaining information related to extrudability and shape fidelity from rheological properties. Overall, this review aims to highlight the transformative potential of SSE 3DP in the pharmaceutical landscape, paving the way for tailored, and patient-centric therapies.

Understanding the complexity of near-infrared quantification of highly porous patient-tailored drug products by utilizing chemometrics and stimulated Raman imaging

Additively manufactured drug products, typically produced using small-scale, on-demand batch mode, require rapid and non-destructive quantification methods. A tunable modular design (TMD) approach combining porous polymeric freeze-dried modules and an additive manufacturing method, inkjet printing, was proposed in an earlier study to fabricate accurate and patient-tailored doses of an antidepressant citalopram hydrobromide. This approach addresses the unmet medical needs associated with antidepressant tapering. Non-destructive quantification of printed porous structures is challenging due to the presence of residual solvents and frequent fluctuation of the material density. These shortcomings were mitigated by utilizing a spinning near-infrared spectroscopy (NIRS) measurement setup and a post-print drying step. A machine learning algorithm (ML), specifically support vector regression, was implemented to lessen potential non-linearities caused by the complex structure of TMD drug products. The non-linear support vector regression models performed better than linear partial least squares (PLS) models when modeling the entire sample set (prediction error improved by 19 %). By dividing the TMD samples into subtypes and creating individual models for each subtype improved model performance: linear PLS models performed better or equally to non-linear models. It was hypothesized that this outcome was due to the structural differences between different TMD sample subtypes that was later confirmed by stimulated Raman scattering (SRS) microscopy. It was demonstrated that for complex porous drug products ML algorithms can improve NIRS model performance when a single universal robust model is preferred, and SRS is a powerful tool to explain the challenges that printing onto porous drug products can introduce to the NIRS quantification.

3D printed dosage forms, where are we headed?

INTRODUCTION

3D Printing (3DP) is an innovative fabrication technology that has gained enormous popularity through its paradigm shifts in manufacturing in several disciplines, including healthcare. In this past decade, we have witnessed the impact of 3DP in drug product development. Almost 8 years after the first USFDA approval of the 3D printed tablet Levetiracetam (Spritam), the interest in 3DP for drug products is high. However, regulatory agencies have often questioned its large-scale industrial practicability, and 3DP drug approval/guidelines are yet to be streamlined.

AREAS COVERED

In this review, major technologies involved with the fabrication of drug products are introduced along with the prospects of upcoming technologies, including AI (Artificial Intelligence). We have touched upon regulatory updates and discussed the burning limitations, which require immediate focus, illuminating status, and future perspectives on the near future of 3DP in the pharmaceutical field.

EXPERT OPINION

3DP offers significant advantages in rapid prototyping for drug products, which could be beneficial for personalizing patient-based pharmaceutical dispensing. It seems inevitable that the coming decades will be marked by exponential growth in personalization, and 3DP could be a paradigm-shifting asset for pharmaceutical professionals.

Preparation and In Vitro Evaluation of Montelukast Sodium-Loaded 3D Printed Orodispersible Films for the Treatment of Asthma

This research aims to produce orodispersible films (ODFs) and determine their potential use in the oral delivery of montelukast sodium for asthma treatment and allergic rhinitis. ODFs were successfully developed by Three-dimensional (3D) printing using propylene glycol (PG), and hydroxypropyl methylcellulose (HPMC), polyethylene glycol 400 (PEG). Finally, the amount of montelukast sodium in the ODFs was 5% (w/w). Drug-excipients compatibility with Fourier Transformed Infrared (FTIR) spectroscopy, mass uniformity, thickness, disintegration time, folding endurance, moisture absorption, pH, in vitro drug release (dissolution), drug content, moisture loss, moisture content, mechanical properties, and cytotoxicity studies were performed on the prepared films. All formulations disintegrated in approximately 40 s. Over 98% of drug release from all films within 2 min was confirmed. It was reported that Fm1-4 (8% HPMC and 1% PEG) and Fm2-4 (10% HPMC and 3% PEG) are more suitable for drug content, but Fm2-4 may be the ideal formulation considering its durability and transportability properties. Based on the characterization results and in vitro release values, the montelukast sodium ODF can be an option for other dosage forms. It was concluded that the formulations did not show toxic potential by in vitro cytotoxicity study with 3T3 cells. This new formulation can efficiently treat allergic rhinitis and asthma diseases.

Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine

Medicines remain ineffective for over 50% of patients due to conventional mass production methods with fixed drug dosages. Three-dimensional (3D) printing, specifically selective laser sintering (SLS), offers a potential solution to this challenge, allowing the manufacturing of small, personalized batches of medication. Despite its simplicity and suitability for upscaling to large-scale production, SLS was not designed for pharmaceutical manufacturing and necessitates a time-consuming, trial-and-error adaptation process. In response, this study introduces a deep learning model trained on a variety of features to identify the best feature set to represent drugs and polymeric materials for the prediction of the printability of drug-loaded formulations using SLS. The proposed model demonstrates success by achieving 90% accuracy in predicting printability. Furthermore, explainability analysis unveils materials that facilitate SLS printability, offering invaluable insights for scientists to optimize SLS formulations, which can be expanded to other disciplines. This represents the first study in the field to develop an interpretable, uncertainty-optimized deep learning model for predicting the printability of drug-loaded formulations. This paves the way for accelerating formulation development, propelling us into a future of personalized medicine with unprecedented manufacturing precision.

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML-related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM-related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM-related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM-related research areas.

Cold Laser Sintering of Medicines: Toward Carbon Neutral Pharmaceutical Printing

Selective laser sintering (SLS) is an emerging three-dimensional (3D) printing technology that uses a laser to fuse powder particles together, which allows the fabrication of personalized solid dosage forms. It possesses great potential for commercial use. However, a major drawback of SLS is the need to heat the powder bed while printing; this leads to high energy consumption (and hence a large carbon footprint), which may hinder its translation to industry. In this study, the concept of cold laser sintering (CLS) is introduced. In CLS, the aim is to sinter particles without heating the powder bed, where the energy from the laser, alone, is sufficient to fuse adjacent particles. The study demonstrated that a laser power above 1.8 W was sufficient to sinter both KollicoatIR and Eudragit L100-55-based formulations at room temperature. The cold sintering printing process was found to reduce carbon emissions by 99% compared to a commercial SLS printer. The CLS printed formulations possessed characteristics comparable to those made with conventional SLS printing, including a porous microstructure, fast disintegration time, and molecular dispersion of the drug. It was also possible to achieve higher drug loadings than was possible with conventional SLS printing. Increasing the laser power from 1.8 to 3.0 W increased the flexural strength of the printed formulations from 0.6 to 1.6 MPa, concomitantly increasing the disintegration time from 5 to over 300 s. CLS appears to offer a new route to laser-sintered pharmaceuticals that minimizes impact on the environment and is fit for purpose in Industry 5.0.

A comprehensive assessment of machine learning algorithms for enhanced characterization and prediction in orodispersible film development

Orodispersible films (ODFs) have emerged as innovative pharmaceutical dosage forms, offering patient-specific treatment through adjustable dosing and the combination of diverse active ingredients. This expanding field generates vast datasets, requiring advanced analytical techniques for deeper understanding of data itself. Machine learning is becoming an important tool in the rapidly changing field of pharmaceutical research, particularly in drug preformulation studies. This work aims to explore into the application of machine learning methods for the analysis of experimental data obtained by ODF characterization in order to obtain an insight into the factors governing ODF performance and use it as guidance in pharmaceutical development. Using a dataset derived from extensive experimental studies, various machine learning algorithms were employed to cluster and predict critical properties of ODFs. Our results demonstrate that machine learning models, including Support vector machine, Random forest and Deep learning, exhibit high accuracy in predicting the mechanical properties of ODFs, such as flexibility and rigidity. The predictive models offered insights into the complex interaction of formulation variables. This research is a pilot study that highlights the potential of machine learning as a transformative approach in the pharmaceutical field, paving the way for more efficient and informed drug development processes.

The role of artificial intelligence in generating original scientific research

Artificial intelligence (AI) is a revolutionary technology that is finding wide application across numerous sectors. Large language models (LLMs) are an emerging subset technology of AI and have been developed to communicate using human languages. At their core, LLMs are trained with vast amounts of information extracted from the internet, including text and images. Their ability to create human-like, expert text in almost any subject means they are increasingly being used as an aid to presentation, particularly in scientific writing. However, we wondered whether LLMs could go further, generating original scientific research and preparing the results for publication. We taskedGPT-4, an LLM, to write an original pharmaceutics manuscript, on a topic that is itself novel. It was able to conceive a research hypothesis, define an experimental protocol, produce photo-realistic images of 3D printed tablets, generate believable analytical data from a range of instruments and write a convincing publication-ready manuscript with evidence of critical interpretation. The model achieved all this is less than 1 h. Moreover, the generated data were multi-modal in nature, including thermal analyses, vibrational spectroscopy and dissolution testing, demonstrating multi-disciplinary expertise in the LLM. One area in which the model failed, however, was in referencing to the literature. Since the generated experimental results appeared believable though, we suggest that LLMs could certainly play a role in scientific research but with human input, interpretation and data validation. We discuss the potential benefits and current bottlenecks for realising this ambition here.

Rising role of 3D-printing in delivery of therapeutics for infectious disease

Modern drug delivery to tackle infectious disease has drawn close to personalizing medicine for specific patient populations. Challenges include antibiotic-resistant infections, healthcare associated infections, and customizing treatments for local patient populations. Recently, 3D-printing has become a facilitator for the development of personalized pharmaceutic drug delivery systems. With a variety of manufacturing techniques, 3D-printing offers advantages in drug delivery development for controlled, fine-tuned release and platforms for different routes of administration. This review summarizes 3D-printing techniques in pharmaceutics and drug delivery focusing on treating infectious diseases, and discusses the influence of 3D-printing design considerations on drug delivery platforms targeting these diseases. Additionally, applications of 3D-printing in infectious diseases are summarized, with the goal to provide insight into how future delivery innovations may benefit from 3D-printing to address the global challenges in infectious disease.

3D printing of biologics-what has been accomplished to date?

Three-dimensional (3D) printing is a promising approach for the stabilization and delivery of non-living biologics. This versatile tool builds complex structures and customized resolutions, and has significant potential in various industries, especially pharmaceutics and biopharmaceutics. Biologics have become increasingly prevalent in the field of medicine due to their diverse applications and benefits. Stability is the main attribute that must be achieved during the development of biologic formulations. 3D printing could help to stabilize biologics by entrapment, support binding, or crosslinking. Furthermore, gene fragments could be transited into cells during co-printing, when the pores on the membrane are enlarged. This review provides: (i) an introduction to 3D printing technologies and biologics, covering genetic elements, therapeutic proteins, antibodies, and bacteriophages; (ii) an overview of the applications of 3D printing of biologics, including regenerative medicine, gene therapy, and personalized treatments; (iii) information on how 3D printing could help to stabilize and deliver biologics; and (iv) discussion on regulations, challenges, and future directions, including microneedle vaccines, novel 3D printing technologies and artificial-intelligence-facilitated research and product development. Overall, the 3D printing of biologics holds great promise for enhancing human health by providing extended longevity and enhanced quality of life, making it an exciting area in the rapidly evolving field of biomedicine.

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

Revolutionizing drug formulation development: The increasing impact of machine learning

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

Preparation and Evaluation of Caffeine Orodispersible Films: The Influence of Hydrotropic Substances and Film-Forming Agent Concentration on Film Properties

This study aimed to develop caffeine (CAF) orodispersible films (ODFs) and verify the effects of different percentages of film-forming agent and hydrotropic substances (citric acid-CA or sodium benzoate-SB) on various film properties. Hydroxypropyl methylcellulose E 5 (HPMC E 5) orodispersible films were prepared using the solvent casting method. Four CAF-ODF formulations were prepared and coded as CAF1 (8% HPMC E 5, CAF), CAF2 (8% HPMC E 5 and CAF:CA-1:1), CAF3 (9% HPMC E 5 and CAF:CA-1:1), and CAF4 (9% HPMC E 5 and CAF:SB-1:1). The CAF-ODFs were evaluated in terms of disintegration time, folding endurance, thickness, uniformity of mass, CAF content, thickness-normalized tensile strength, adhesiveness, dissolution, and pH. Thin, opaque, and slightly white CAF-ODFs were obtained. All the formulations developed exhibited disintegration times less than 3 min. The dissolution test revealed that CAF1, CAF2, and CAF3 exhibited concentrations of active pharmaceutical ingredients (APIs) released at 30 min that were close to 100%, whilst CAF4 showed a faster dissolution behaviour (100% of the CAF was released at 5 min). Thin polymeric films containing 10 mg of CAF/surface area (3.14 cm²) were prepared.

Energy Consumption and Carbon Footprint of 3D Printing in Pharmaceutical Manufacture

Achieving carbon neutrality is seen as an important goal in order to mitigate the effects of climate change, as carbon dioxide is a major greenhouse gas that contributes to global warming. Many countries, cities and organizations have set targets to become carbon neutral. The pharmaceutical sector is no exception, being a major contributor of carbon emissions (emitting approximately 55% more than the automotive sector for instance) and hence is in need of strategies to reduce its environmental impact. Three-dimensional (3D) printing is an advanced pharmaceutical fabrication technology that has the potential to replace traditional manufacturing tools. Being a new technology, the environmental impact of 3D printed medicines has not been investigated, which is a barrier to its uptake by the pharmaceutical industry. Here, the energy consumption (and carbon emission) of 3D printers is considered, focusing on technologies that have successfully been demonstrated to produce solid dosage forms. The energy consumption of 6 benchtop 3D printers was measured during standby mode and printing. On standby, energy consumption ranged from 0.03 to 0.17 kWh. The energy required for producing 10 printlets ranged from 0.06 to 3.08 kWh, with printers using high temperatures consuming more energy. Carbon emissions ranged between 11.60-112.16 g CO₂ (eq) per 10 printlets, comparable with traditional tableting. Further analyses revealed that decreasing printing temperature was found to reduce the energy demand considerably, suggesting that developing formulations that are printable at lower temperatures can reduce CO₂ emissions. The study delivers key initial insights into the environmental impact of a potentially transformative manufacturing technology and provides encouraging results in demonstrating that 3D printing can deliver quality medicines without being environmentally detrimental.

Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems

A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been introduced to Additive Manufacturing (AM) and Microfluidics (MFs) to predict and generate learning patterns for precise fabrication of tailor-made pharmaceutical treatments. Moreover, regarding the diversity and complexity of personalised medicine, ML has been part of quality by design strategy, targeting towards the development of safe and effective drug delivery systems. The utilisation of different and novel ML techniques along with Internet of Things sensors in AM and MFs, have shown promising aspects regarding the development of well-defined automated procedures towards the production of sustainable and quality-based therapeutic systems. Thus, the effective data utilisation, prospects on a flexible and broader production of "on demand" treatments. In this study, a thorough overview has been achieved, concerning scientific achievements of the past decade, which aims to trigger the research interest on incorporating different types of ML in AM and MFs, as essential techniques for the enhancement of quality standards of customised medicinal applications, as well as the reduction of variability potency, throughout a pharmaceutical process.

Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products

Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for its high precision, and compatibility with a wide range of pharmaceutical materials, including low-solubility compounds. However, the full potential of SLS for medicines is yet to be realized, requiring expertise and considerable time-consuming and resource-intensive trial-and-error research. Machine learning (ML), a subset of artificial intelligence, is an in silico tool that is accomplishing remarkable breakthroughs in several sectors for its ability to make highly accurate predictions. Therefore, the present study harnessed ML to predict the printability of SLS formulations. Using a dataset of 170 formulations from 78 materials, ML models were developed from inputs that included the formulation composition and characterization data retrieved from Fourier-transformed infrared spectroscopy (FT-IR), X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). Multiple ML models were explored, including supervised and unsupervised approaches. The results revealed that ML can achieve high accuracies, by using the formulation composition leading to a maximum F1 score of 81.9%. Using the FT-IR, XRPD and DSC data as inputs resulted in an F1 score of 84.2%, 81.3%, and 80.1%, respectively. A subsequent ML pipeline was built to combine the predictions from FT-IR, XRPD and DSC into one consensus model, where the F1 score was found to further increase to 88.9%. Therefore, it was determined for the first time that ML predictions of 3D printability benefit from multi-modal data, combining numeric, spectral, thermogram and diffraction data. The study lays the groundwork for leveraging existing characterization data for developing high-performing computational models to accelerate formulation development.

Buccal films: A review of therapeutic opportunities, formulations & relevant evaluation approaches

The potential of the mucoadhesive film technology is hard to ignore, owing to perceived superior patient acceptability versus buccal tablets, and significant therapeutic opportunities compared to conventional oral drug delivery systems, especially for those who suffer from dysphagia. In spite of this, current translation from published literature into the commercial marketplace is virtually non-existent, with no authorised mucoadhesive buccal films available in the UK and very few available in the USA. This review seeks to provide an overview of the mucoadhesive buccal film technology and identify key areas upon which to focus scientific efforts to facilitate the wider adoption of this patient-centric dosage form. Several indications and opportunities for development were identified, while discussing the patient-related factors influencing the use of these dosage forms. In addition, an overview of the technologies behind the manufacturing of these films was provided, highlighting manufacturing methods like solvent casting, hot melt extrusion, inkjet printing and three-dimensional printing. Over thirty mucoadhesive polymers were identified as being used in film formulations, with details surrounding their mucoadhesive capabilities as well as their inclusion alongside other key formulation constituents provided. Lastly, the importance of physiologically relevant in vitro evaluation methodologies was emphasised, which seek to improve in vivo correlations, potentially leading to better translation of mucoadhesive buccal films from the literature into the commercial marketplace.

Modernising Orodispersible Film Characterisation to Improve Palatability and Acceptability Using a Toolbox of Techniques

Orodispersible films (ODFs) have been widely used in paediatric, geriatric and dysphagic patients due to ease of administration and precise and flexible dose adjustments. ODF fabrication has seen significant advancements with the move towards more technologically advanced production methods. The acceptability of ODFs is dependent upon film composition and process of formation, which affects disintegration, taste, texture and mouthfeel. There is currently a lack of testing to accurately assess ODFs for these important acceptability sensory perceptions. This study produced four ODFs formed of polyvinyl alcohol and sodium carboxymethylcellulose using 3D printing. These were assessed using three in vitro methods: Petri dish and oral cavity model (OCM) methods for disintegration and bio-tribology for disintegration and oral perception. Increasing polymer molecular weight (MW) exponentially increased disintegration time in the Petri dish and OCM methods. Higher MW films adhered to the OCM upper palate. Bio-tribology analysis showed that films of higher MW disintegrated quickest and had lower coefficient of friction, perhaps demonstrating good oral perception but also stickiness, with higher viscosity. These techniques, part of a toolbox, may enable formulators to design, test and reformulate ODFs that both disintegrate rapidly and may be better perceived when consumed, improving overall treatment acceptability.

Advancing pharmacy and healthcare with virtual digital technologies

Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.